Statistical analysis with missing data
Statistical analysis with missing data
Unknown attribute values in induction
Proceedings of the sixth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Hybrid inductive machine learning: an overview of CLIP algorithms
New learning paradigms in soft computing
Machine Learning
Machine Learning
A Comparison of Several Approaches to Missing Attribute Values in Data Mining
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Classifying Unseen Cases with Many Missing Values
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Techniques for Dealing with Missing Values in Classification
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
CLIP4: hybrid inductive machine learning algorithm that generates inequality rules
Information Sciences: an International Journal - Special issue: Soft computing data mining
A Recursive Partitioning Decision Rule for Nonparametric Classification
IEEE Transactions on Computers
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
A rough set approach to data with missing attribute values
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A DS-AHP approach for multi-attribute decision making problem with incomplete information
Expert Systems with Applications: An International Journal
Rough sets based association rules application for knowledge-based system design
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Time series AR modeling with missing observations based on the polynomial transformation
Mathematical and Computer Modelling: An International Journal
Optimum estimation of missing values in randomized complete block design by genetic algorithm
Knowledge-Based Systems
A data mining driven risk profiling method for road asset management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Missing values: how many can they be to preserve classification reliability?
Artificial Intelligence Review
| Hi-index | 0.98 |
In this paper, a new approach to working with missing attribute values in inductive learning algorithms is introduced. Three fundamental issues are studied: the splitting criterion, the allocation of values to missing attribute values, and the prediction of new observations. The formal definition for the splitting criterion is given. This definition takes into account the missing attribute values and generalizes the classical definition. In relation to the second objective, multiple values are assigned to missing attribute values using a decision theory approach. Each of these multiple values will have an associated confidence and error parameter. The error parameter measures how near or how far the value is from the original value of the attribute. After applying a splitting criterion, a decision tree is obtained (from training sets with or without missing attribute values). This decision tree can be used to predict the class of an observation (with or without missing attribute values). Hence, there are four perspectives. The three perspectives with missing attribute values are studied and experimental results are presented.